248 research outputs found
Tropical Cyclone Intensity Estimation Using Multi-Dimensional Convolutional Neural Networks from Geostationary Satellite Data
For a long time, researchers have tried to find a way to analyze tropical cyclone (TC) intensity in real-time. Since there is no standardized method for estimating TC intensity and the most widely used method is a manual algorithm using satellite-based cloud images, there is a bias that varies depending on the TC center and shape. In this study, we adopted convolutional neural networks (CNNs) which are part of a state-of-art approach that analyzes image patterns to estimate TC intensity by mimicking human cloud pattern recognition. Both two dimensional-CNN (2D-CNN) and three-dimensional-CNN (3D-CNN) were used to analyze the relationship between multi-spectral geostationary satellite images and TC intensity. Our best-optimized model produced a root mean squared error (RMSE) of 8.32 kts, resulting in better performance (~35%) than the existing model using the CNN-based approach with a single channel image. Moreover, we analyzed the characteristics of multi-spectral satellite-based TC images according to intensity using a heat map, which is one of the visualization means of CNNs. It shows that the stronger the intensity of the TC, the greater the influence of the TC center in the lower atmosphere. This is consistent with the results from the existing TC initialization method with numerical simulations based on dynamical TC models. Our study suggests the possibility that a deep learning approach can be used to interpret the behavior characteristics of TCs
Deep Learning Techniques in Extreme Weather Events: A Review
Extreme weather events pose significant challenges, thereby demanding
techniques for accurate analysis and precise forecasting to mitigate its
impact. In recent years, deep learning techniques have emerged as a promising
approach for weather forecasting and understanding the dynamics of extreme
weather events. This review aims to provide a comprehensive overview of the
state-of-the-art deep learning in the field. We explore the utilization of deep
learning architectures, across various aspects of weather prediction such as
thunderstorm, lightning, precipitation, drought, heatwave, cold waves and
tropical cyclones. We highlight the potential of deep learning, such as its
ability to capture complex patterns and non-linear relationships. Additionally,
we discuss the limitations of current approaches and highlight future
directions for advancements in the field of meteorology. The insights gained
from this systematic review are crucial for the scientific community to make
informed decisions and mitigate the impacts of extreme weather events
Deep Learning Methods for Remote Sensing
Remote sensing is a field where important physical characteristics of an area are exacted using emitted radiation generally captured by satellite cameras, sensors onboard aerial vehicles, etc. Captured data help researchers develop solutions to sense and detect various characteristics such as forest fires, flooding, changes in urban areas, crop diseases, soil moisture, etc. The recent impressive progress in artificial intelligence (AI) and deep learning has sparked innovations in technologies, algorithms, and approaches and led to results that were unachievable until recently in multiple areas, among them remote sensing. This book consists of sixteen peer-reviewed papers covering new advances in the use of AI for remote sensing
CNN Profiler on Polar Coordinate Images for Tropical Cyclone Structure Analysis
Convolutional neural networks (CNN) have achieved great success in analyzing
tropical cyclones (TC) with satellite images in several tasks, such as TC
intensity estimation. In contrast, TC structure, which is conventionally
described by a few parameters estimated subjectively by meteorology
specialists, is still hard to be profiled objectively and routinely. This study
applies CNN on satellite images to create the entire TC structure profiles,
covering all the structural parameters. By utilizing the meteorological domain
knowledge to construct TC wind profiles based on historical structure
parameters, we provide valuable labels for training in our newly released
benchmark dataset. With such a dataset, we hope to attract more attention to
this crucial issue among data scientists. Meanwhile, a baseline is established
with a specialized convolutional model operating on polar-coordinates. We
discovered that it is more feasible and physically reasonable to extract
structural information on polar-coordinates, instead of Cartesian coordinates,
according to a TC's rotational and spiral natures. Experimental results on the
released benchmark dataset verified the robustness of the proposed model and
demonstrated the potential for applying deep learning techniques for this
barely developed yet important topic.Comment: Submitted to AAAI202
Remote Sensing of the Oceans
This book covers different topics in the framework of remote sensing of the oceans. Latest research advancements and brand-new studies are presented that address the exploitation of remote sensing instruments and simulation tools to improve the understanding of ocean processes and enable cutting-edge applications with the aim of preserving the ocean environment and supporting the blue economy. Hence, this book provides a reference framework for state-of-the-art remote sensing methods that deal with the generation of added-value products and the geophysical information retrieval in related fields, including: Oil spill detection and discrimination; Analysis of tropical cyclones and sea echoes; Shoreline and aquaculture area extraction; Monitoring coastal marine litter and moving vessels; Processing of SAR, HF radar and UAV measurements
Forecasting formation of a Tropical Cyclone Using Reanalysis Data
The tropical cyclone formation process is one of the most complex natural
phenomena which is governed by various atmospheric, oceanographic, and
geographic factors that varies with time and space. Despite several years of
research, accurately predicting tropical cyclone formation remains a
challenging task. While the existing numerical models have inherent
limitations, the machine learning models fail to capture the spatial and
temporal dimensions of the causal factors behind TC formation. In this study, a
deep learning model has been proposed that can forecast the formation of a
tropical cyclone with a lead time of up to 60 hours with high accuracy. The
model uses the high-resolution reanalysis data ERA5 (ECMWF reanalysis 5th
generation), and best track data IBTrACS (International Best Track Archive for
Climate Stewardship) to forecast tropical cyclone formation in six ocean basins
of the world. For 60 hours lead time the models achieve an accuracy in the
range of 86.9% - 92.9% across the six ocean basins. The model takes about 5-15
minutes of training time depending on the ocean basin, and the amount of data
used and can predict within seconds, thereby making it suitable for real-life
usage
Spatial-Temporal Data Mining for Ocean Science: Data, Methodologies, and Opportunities
With the increasing amount of spatial-temporal~(ST) ocean data, numerous
spatial-temporal data mining (STDM) studies have been conducted to address
various oceanic issues, e.g., climate forecasting and disaster warning.
Compared with typical ST data (e.g., traffic data), ST ocean data is more
complicated with some unique characteristics, e.g., diverse regionality and
high sparsity. These characteristics make it difficult to design and train STDM
models. Unfortunately, an overview of these studies is still missing, hindering
computer scientists to identify the research issues in ocean while discouraging
researchers in ocean science from applying advanced STDM techniques. To remedy
this situation, we provide a comprehensive survey to summarize existing STDM
studies in ocean. Concretely, we first summarize the widely-used ST ocean
datasets and identify their unique characteristics. Then, typical ST ocean data
quality enhancement techniques are discussed. Next, we classify existing STDM
studies for ocean into four types of tasks, i.e., prediction, event detection,
pattern mining, and anomaly detection, and elaborate the techniques for these
tasks. Finally, promising research opportunities are highlighted. This survey
will help scientists from the fields of both computer science and ocean science
have a better understanding of the fundamental concepts, key techniques, and
open challenges of STDM in ocean
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